inst/doc/WVPlots_concept.R

In WVPlots: Common Plots for Analysis

## ----echo=FALSE---------------------------------------------------------------
knitr::opts_chunk$set(fig.width=6, fig.height=6)
if (requireNamespace('data.table', quietly = TRUE)) {
	# don't multi-thread during CRAN checks
	data.table::setDTthreads(1)
}

## -----------------------------------------------------------------------------
set.seed(52523)
d <- data.frame(
  wt = 100*rnorm(100),
  stringsAsFactors = FALSE)
WVPlots::PlotDistCountNormal(d,'wt','example')

## -----------------------------------------------------------------------------
WVPlots::PlotDistDensityNormal(d,'wt','example')

## -----------------------------------------------------------------------------
set.seed(34903490)
x = rnorm(50)
y = 0.5*x^2 + 2*x + rnorm(length(x))
frm = data.frame(
  x=x,
  y=y,
  yC=y>=as.numeric(quantile(y,probs=0.8)),
  stringsAsFactors = FALSE)
frm$absY <- abs(frm$y)
frm$posY = frm$y > 0
WVPlots::ScatterHist(frm, "x", "y", smoothmethod="lm", 
                     title="Example Linear Fit")

## -----------------------------------------------------------------------------
set.seed(34903490)
y = abs(rnorm(20)) + 0.1
x = abs(y + 0.5*rnorm(20))

frm = data.frame(
  model=x, 
  value=y,
  stringsAsFactors = FALSE)

frm$costs=1
frm$costs[1]=5
frm$rate = with(frm, value/costs)

frm$isValuable = (frm$value >= as.numeric(quantile(frm$value, probs=0.8)))
gainx = 0.10  # get the top 10% most valuable points as sorted by the model

# make a function to calculate the label for the annotated point
labelfun = function(gx, gy) {
  pctx = gx*100
  pcty = gy*100
  
  paste("The top ", pctx, "% most valuable points by the model\n",
        "are ", pcty, "% of total actual value", sep='')
}

WVPlots::GainCurvePlotWithNotation(frm, "model", "value", 
                                   title="Example Gain Curve with annotation", 
                          gainx=gainx,labelfun=labelfun) 

## -----------------------------------------------------------------------------
set.seed(52523)
d = data.frame(
  meas=rnorm(100),
  stringsAsFactors = FALSE)
threshold = 1.5
WVPlots::ShadedDensity(d, "meas", threshold, tail="right", 
                       title="Example shaded density plot, right tail")


## -----------------------------------------------------------------------------
set.seed(34903490)
frm = data.frame(
  x=rnorm(50),
  y=rnorm(50),
  stringsAsFactors = FALSE)
frm$z <- frm$x+frm$y
WVPlots::ScatterHistN(frm, "x", "y", "z", title="Example Joint Distribution")

## -----------------------------------------------------------------------------
set.seed(34903490)
x = rnorm(50)
y = 0.5*x^2 + 2*x + rnorm(length(x))
frm = data.frame(
  x = x,
  yC = y>=as.numeric(quantile(y,probs=0.8)),
  stringsAsFactors = FALSE)
WVPlots::ROCPlot(frm, "x", "yC", TRUE, title="Example ROC plot")